Microarray and bioinformatic analysis of conventional ameloblastoma: an observational analysis

Abstract Ameloblastoma is a highly aggressive odontogenic tumor, and its pathogenesis is associated with many participating genes. Objective We aimed to identify and validate new critical genes of conventional ameloblastoma using microarray and bioinformatics analysis. Methodology Gene expression microarray and bioinformatic analysis were performed using CHIP H10KA and DAVID software for enrichment. Protein-protein interactions (PPI) were visualized using STRING-Cytoscape with MCODE plugin, followed by Kaplan-Meier and GEPIA analyses that were used for the candidate’s postulation. RT-qPCR and IHC assays were performed to validate the bioinformatic approach. Results 376 upregulated genes were identified. PPI analysis revealed 14 genes that were validated by Kaplan-Meier and GEPIA resulting in PDGFA and IL2RA as candidate genes. The RT-qPCR analysis confirmed their intense expression. Immunohistochemistry analysis showed that PDGFA expression is parenchyma located. Conclusion With bioinformatics methods, we can identify upregulated genes in conventional ameloblastoma, and with RT-qPCR and immunoexpression analysis validate that PDGFA could be a more specific and localized therapeutic target.


Introduction
Odontogenic tumors (OTs) are oral lesions that impact the quality of life of patients because they affect not only the teeth but the maxilla and mandible.
OTs constitute a group of heterogeneous diseases ranging from hamartomatous lesions to benign and malignant neoplasms with metastatic potential. These are derived from the epithelium, ectomesenchyme, and/or mesenchymal elements of the odontogenesis apparatus. 1 The epidemiology of OTs varies throughout the world; in some countries, the most frequent OT is ameloblastoma (Hong Kong, Japan, Zimbabwe, and Nigeria), whereas in others (United States of America, Brazil, and Canada) the most frequent tumor is odontoma. 1,2 The most common OT in Mexico is odontoma, followed by ameloblastoma, myxoma, adenomatoid odontogenic tumors, and calcifying odontogenic cysts. 3 Ameloblastoma is a slow-growing locally invasive benign OT with different histological variants, which may be located in the posterior zone of the mandible or maxilla. Because of its potential for recurrence, it is often classified as an aggressive tumor. The estimated global incidence of ameloblastoma is 0.5 cases per million people per year, with the age of diagnosis ranging from 30-60 years. Conventional ameloblastoma (CAm) is the most common variant, followed by unicystic and peripheral ameloblastoma. Although the precise etiology of CAm is unknown, dysregulation of many genes associated with odontogenesis is speculated to play an important role in its histogenesis. 2,4 Changes in the expression or mutations in genes, such as BRAF, Ras, FGFR2, and SMO, among others, could be associated with its histogenesis. 5,6 Given this complexity, high-throughput assays offer an alternative to comprehensively analyze this neoplasm. Microarray technology has been used to obtain information on the genetic alterations that occur in several diseases, including neoplasms, such as CAm. Much data are obtained with high-throughput analysis and integrated bioinformatics methods are necessary to unravel the mechanisms underlying the pathogenesis of diseases and to explore and identify novel biomarkers that could help us in further studies. 7,8 Previous approaches with microarrays assays had shown elements of SHH, cell-cycle regulation, inflammation, MAP kinase pathways, and other molecules, which were confirmed via tests, such as PCR, immunohistochemistry, or NanoString, suggesting that these regulators are important elements of the pathogenesis of conventional ameloblastoma. [9][10][11] The bioinformatic analysis is only the first step for new biomarkers to be proposed. Those must be corroborated with particular assays, so that this information can cross over to the clinical level. 12 The objective was to identify and validate new critical genes of conventional ameloblastoma using microarray and bioinformatics analysis. 10,000 ×g for 15 s. The entire lower (aqueous) phase was transferred to a binding column placed in a tube and the assembly was centrifuged at 10,000 ×g for 30 s, washed twice with 500 μL of 1× wash solution, and centrifuged at 10,000 ×g for 30 s. The centrifuge column was dried at 16,000 ×g for 3 min and the RNA was eluted in 50 μL of nuclease-free water centrifuge by centrifuging at 16,000 ×g for 1 min. RNA concentration and purity were determined using a NanoDrop ND-2000 spectrophotometer (Thermo Fisher, Rochester, NY, USA) considering only samples with >1.8 260/280 ratio. RNA integrity was evaluated using agarose gel electrophoresis. All ameloblastoma and dental follicle RNA samples were pooled into a single sample to obtain 2 μg RNA each for the synthesis of cDNA that was used for the microarray.

Methodology
The cDNA from dental follicle control was labeled with Alexa 555, and that from CAm was labeled with

Protein-protein interaction (PPI)
The Search Tool for the Retrieval of Interacting Genes (STRING); version 11.0, http://string-db.org/ database was used to predict the protein-protein interaction (PPI) networks of the DEGs. 17 Then, the Cytoscape software was used to analyze the interaction with a combined score of >0.4 (http://cytoscape. org). Finally, the plugin molecular complex detection (MCODE) was used to screen the most significant module in the PPI networks with the MCODE score >, degree cutoff=2, node score cutoff=0.2, k-core 2, and max depth=100.

Selection and analyses of hub genes
For the selection of the hub genes, those clustered with MCODE score ≥ 2.5 were selected, and then the effect of the hub genes on overall survival and disease-  For the analysis of each marker, photomicrographs of 5 fields were obtained at 400× magnification from each sample using a Leica ICC50 HD camera. The intensity of staining (optical density) was obtained using the ImageJ software (NIH, Bethesda MD, USA); calibrating the quantification to establish the scale of optical density at: 0-0.9/negative, 1-1.9/mild, 2-2.9/ moderate, and >3/intense.

Results
The gender distribution was nine males and six females. The mean age was 37.8±17.7 years old.
In total, 12 conventional ameloblastoma presented follicular and three plexiform patterns. All specimens were located in the mandible (Supplementary Table 1).
Although the quality of the RNA samples was high, the amount of RNA was insufficient to perform independent microarray analyses. Thus, the RNA samples from ameloblastoma and dental follicles were pooled to obtain ameloblastoma and dental follicle control groups, respectively.

Identification of DEG gene ontology and KEGG pathway analysis
In total, 376 upregulated genes were identified.  We then applied Cytoscape MCODE for further analysis, and the results showed 25 nodes and 68 edges, with 20 clustered genes with an MCODE score >2.5 (Figure 1).

Analysis of core genes using Kaplan-Meier plotter and GEPIA
The Kaplan-Meier plotter was used to identify the survival data for these 20 clustered genes. Only nine genes were significantly associated with poor survival (Figure 2). GEPIA was used to validate these nine genes and led to the identification of two genes (plateletderived growth factor A (PDGFA) and interleukin 2 receptor subunit alpha (IL2RA) with significant correlation (Figure 3).

IL2RA and PDGFA gene expression and immunohistochemistry analysis
Three samples showed a follicular pattern and five were plexiform (Supplementary Table 2). The gene expressions of IL2RA and PDGFA in conventional ameloblastoma were higher than those in the dental follicle in the 2^-(∆∆Ct) method relative quantification with 362±66 and 419±33 measure units, respectively.  Table 2-KEGG pathway analysis of differential expressed genes associated with ameloblastoma The immunoexpression analysis showed that IL2RA presented an intense expression in the parenchyma and stroma of CAm, especially in the follicular pattern. The PDGFA showed a moderate to mild immunoexpression in plexiform and follicular patterns respectively, predominantly in the parenchyma, however, there was no significant difference related to histological pattern ( Figure 4).

Discussion
Conventional ameloblastoma is a benign epithelial odontogenic tumor that is frequently diagnosed in young adults with a median age of 35 years without any gender-specific trend. CAm often progresses slowly but is locally invasive. Untreated tumors resorb the cortical plate bone and extend into the adjacent tissue. 5,20 Our samples used for microarray were obtained from 15 patients (six females and nine males) with a mean age of 37.2±17.8 years, which is consistent with that reported previously. 20 However, it has been proposed that when mutation BRAF V600E is present, the presentation age is earlier than the wildtype genotype. 2 In Mexico, CAm is commonly diagnosed in advanced stages due to the absence of symptoms and low prevalence, which results in detrimental effects on the bone as described above, thus complicating Figure 2-A) Prognostic information of the 20 core genes. Kaplan-Meier plotter online tool was used to analyze the prognostic information and nine genes were found to be significantly associated with survival rate (*p<0.05). B) Validation of the significant genes by GEPIA. The significant genes expressed in patients with ameloblastoma were compared to those in healthy individuals. Only platelet-derived growth factor A (PDGFA) and interleukin 2 receptor subunit alpha (IL2RA) showed significant differential expression (*p<0.05)

Category Genes
Prognostic information of the 20 key candidate genes analyzed by It has been hypothesized that ameloblastoma cells and stromal fibroblasts may be reciprocally activated via cytokines, such as IL-6, IL-8, IL-1a, and recently IL33, to create a tumoral microenvironment that promotes tumor formation. 8,34 Damoiseaux 35 (2020) has reported that the IL2RA fraction can function in a diverse way to lead to leukocyte activation via paracellular or even soluble forms, which can affect the functionality of cells such as cytotoxic CD4 and CD8 T lymphocytes at the tumor level. This is the first report to identify IL2RA as a possible participant in the mechanism underlying the development of ameloblastoma. When we analyzed the KM plot result, we observed that patients with a high level of IL2RA have a greater probability of survival. If we correlate this result with immunoexpression results, immunomodulation by IL2RA is present, but additional studies would need to verify the mechanism that conducts it, as well as the J Appl Oral Sci. 2022;30:e20220308 10/11 result of PDGFA, since due to its greater tendency to express itself in tumor parenchyma, it becomes a direct target. Nevertheless, the validation analysis of IL2RA and PDGFA by immunohistochemistry reinforces the concept that the parenchyma and stroma relationship is a necessary feature that must be considered to improve our understanding, in order to develop better therapeutic strategies. Zhang,et al. 36 (2022) suggested in a bioinformatic analysis that macrophages could infiltrate the ameloblastoma and participate in their pathogenesis. 36 That could be a relationship mainly with IL2RA, however, to prove this immunological relationship their validation is necessary.
Taken together, our bioinformatic analysis identified two hub genes (PDGFA and IL2RA) between CAm and normal dental follicles. The results suggested that these genes play key roles in the pathogenesis, progression, and prognosis of CAm. The main limitation to postulating PDGFA and IL2RA as therapeutic targets is the verification of their reach in cellular or animal models, in which the biological behavior could be measured and correlated. For this reason, identifying how we can affect CAm in these specific targets to provide useful information on these new biomarkers is necessary.